Automated Methods of Technical Skill Assessment in Surgery: A Systematic Review

Levin, Marc; McKechnie, Tyler; Khalid, Shuja; Grantcharov, Teodor P.; Goldenberg, Mitchell

doi:10.1016/j.jsurg.2019.06.011

Cited by 70 publications

(64 citation statements)

References 24 publications

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“…Methods of assessment can be divided into global and procedure/task-specific assessments, with several different assessment tools that have been validated for the purposes of distinguishing experienced and inexperienced surgeons. Kinematic approaches such as assessment of motion have also been used to differentiate experienced from novice surgeons and to track the learning curve [29,30].…”

Section: Challenges In Annotating Surgical Performancementioning

confidence: 99%

Challenges in surgical video annotation

Ward

Fer

Ban

et al. 2021

Computer Assisted Surgery

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Annotation of surgical video is important for establishing ground truth in surgical data science endeavors that involve computer vision. With the growth of the field over the last decade, several challenges have been identified in annotating spatial, temporal, and clinical elements of surgical video as well as challenges in selecting annotators. In reviewing current challenges, we provide suggestions on opportunities for improvement and possible next steps to enable translation of surgical data science efforts in surgical video analysis to clinical research and practice.

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Section: Challenges In Annotating Surgical Performancementioning

confidence: 99%

Challenges in surgical video annotation

Ward

Fer

Ban

et al. 2021

Computer Assisted Surgery

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show abstract

“…Before the advent of machine learning, different feature extraction techniques were used, 64,65 such as Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Principal Component Analysis (PCA) and Codebook. Lately, with the advent of DL, some new interesting solutions have been proposed by the community.…”

Section: Literature Reviewmentioning

confidence: 99%

Intraoperative surgery room management: A deep learning perspective

Tanzi

Piazzolla

Vezzetti

2020

Robotics Computer Surgery

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Purpose: The current study aimed to systematically review the literature addressing the use of deep learning (DL) methods in intraoperative surgery applications, focusing on the data collection, the objectives of these tools and, more technically, the DLbased paradigms utilized. Methods: A literature search with classic databases was performed: we identified, with the use of specific keywords, a total of 996 papers. Among them, we selected 52 for effective analysis, focusing on articles published after January 2015. Results: The preliminary results of the implementation of DL in clinical setting are encouraging. Almost all the surgery sub-fields have seen the advent of artificial intelligence (AI) applications and the results outperformed the previous techniques in the majority of the cases. From these results, a conceptualization of an intelligent operating room (IOR) is also presented. Conclusion: This evaluation outlined how AI and, in particular, DL are revolutionizing the surgery field, with numerous applications, such as context detection and room management. This process is evolving years by years into the realization of an IOR, equipped with technologies perfectly suited to drastically improve the surgical workflow.

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“…Dealing with the inadequacy of the training process, young surgeons as well as experts are relying more on alternative preparatory resources, such as surgical videos, to develop and improve their skills [1,2]. Though the utility of video recordings is proven, their manual annotation and analysis require considerable experience, take a relatively long time and are associated with a high cost [3,4]. Moreover, the handcrafted method cannot work with high performance on raw, preprocessed samples.…”

Section: Introductionmentioning

confidence: 99%

Object and anatomical feature recognition in surgical video images based on a convolutional neural network

et al. 2021

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Purpose Artificial intelligence-enabled techniques can process large amounts of surgical data and may be utilized for clinical decision support to recognize or forecast adverse events in an actual intraoperative scenario. To develop an image-guided navigation technology that will help in surgical education, we explored the performance of a convolutional neural network (CNN)-based computer vision system in detecting intraoperative objects. Methods The surgical videos used for annotation were recorded during surgeries conducted in the Department of Surgery of Tokyo Women's Medical University from 2019 to 2020. Abdominal endoscopic images were cut out from manually captured surgical videos. An open-source programming framework for CNN was used to design a model that could recognize and segment objects in real time through IBM Visual Insights. The model was used to detect the GI tract, blood, vessels, uterus, forceps, ports, gauze and clips in the surgical images. Results The accuracy, precision and recall of the model were 83%, 80% and 92%, respectively. The mean average precision (mAP), the calculated mean of the precision for each object, was 91%. Among surgical tools, the highest recall and precision of 96.3% and 97.9%, respectively, were achieved for forceps. Among the anatomical structures, the highest recall and precision of 92.9% and 91.3%, respectively, were achieved for the GI tract. ConclusionThe proposed model could detect objects in operative images with high accuracy, highlighting the possibility of using AI-based object recognition techniques for intraoperative navigation. Real-time object recognition will play a major role in navigation surgery and surgical education.

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Automated Methods of Technical Skill Assessment in Surgery: A Systematic Review

Cited by 70 publications

References 24 publications

Challenges in surgical video annotation

Challenges in surgical video annotation

Intraoperative surgery room management: A deep learning perspective

Object and anatomical feature recognition in surgical video images based on a convolutional neural network

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